Browsing by Author "Mehdizadeh, Saeid"

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Closure to the discussion of Ebtehaj et al. on “Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach”
(Elsevier B.V., 2021) Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Jan Franklin Adamowski; Fathian, Farshad; Safari, Mir Jafar Sadegh; Mehdizadeh, Saeid; Adamowski, Jan
In this closure we respond to the comments of Ebtehaj et al. (2020) and also provide additional details regarding several features of our study. © 2021 Elsevier B.V. All rights reserved.
Citation - WoS: 52
Citation - Scopus: 56
Comparative assessment of time series and artificial intelligence models to estimate monthly streamflow: A local and external data analysis approach
(Elsevier B.V., 2019) Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Jan Franklin Adamowski; Fathian, Farshad; Safari, Mir Jafar Sadegh; Mehdizadeh, Saeid; Adamowski, Jan F.
River flow rates are important for water resources projects. Given this the current study explored the use of autoregressive (AR) and moving average (MA) techniques as individual time series models and compared them to the same models hybridized with an autoregressive conditional heteroscedasticity (ARCH) model to estimate monthly streamflow. In addition two artificial intelligence (AI) approaches namely multivariate adaptive regression splines (MARS) and gene expression programming (GEP) were explored. The performance of each of these models in estimating monthly streamflow was compared based on local and external data analyses. Using the local data analysis approach streamflow data at each target station was estimated using observed streamflow at the same station. The external data analysis approach used neighboring station streamflow data to estimate streamflow data for the target station. The Beinerahe Roodbar and Pole Astaneh stations on the Sefidrood River Iran as well as the Port Elgin and Walkerton stations on the Saugeen River Canada were used as study areas. Upstream and downstream monthly streamflow time series data were used. The performance of all models was compared using three error metrics including the root mean square error mean absolute error and correlation coefficient. The results showed that the hybrid time series models (i.e. AR-ARCH and MA-ARCH) outperformed the conventional AR and MA models. A comparison of all applied models revealed that the hybrid AR-ARCH and MA-ARCH time series models performed better than the AI techniques when using a local data analysis approach. The external data analysis approach was more accurate for monthly streamflow estimation than the local data analysis approach. To conclude based on the outcomes of the AI models under the external data analysis approach nearby data can be used to estimate streamflow of a target station when the target station streamflow data are not available. © 2019 Elsevier B.V. All rights reserved.
Citation - WoS: 45
Citation - Scopus: 53
Developing novel hybrid models for estimation of daily soil temperature at various depths
(Elsevier B.V., 2020) Saeid Mehdizadeh; Farshad Fathian; Mir Jafar Sadegh Safari; Ali Khosravi; Khosravi, Ali; Fathian, Farshad; Safari, Mir Jafar Sadegh; Mehdizadeh, Saeid
Estimation of soil temperature (ST) as one of the vital parameters of soil which has an impact on many chemical and physical characteristics of soil is of great importance in soil science. This study applies a time series-based model namely fractionally autoregressive integrated moving average (FARIMA) as well as two machine learning-based models consisting of feed-forward back propagation neural networks (FFBPNN) and gene expression programming (GEP) for daily ST estimation. In doing so the daily ST data of three stations at four depths (5 10 50 and 100 cm) in Iran were used for the time period from 1998 to 2017. Studied stations were selected from different climates including arid (Isfahan station) semi-arid (Urmia station) and very humid (Rasht station) to evaluate the performance of models and generalize the outcomes in different climate classes. The performances of the developed models are evaluated via three statistical metrics including the root mean square error (RMSE) mean absolute error (MAE) and relative RMSE (RRMSE). Results obtained demonstrated that the machine learning-based FFBPNN and GEP models performed better than the time series-based FARIMA approach at all depths. As a result negligible differences were observed between the accuracies of FFBPNN and GEP. In addition this study developed novel hybrid models through combining the FFBPNN and GEP techniques with the FARIMA to enhance the accuracy of traditional FARIMA FFBPNN and GEP. The developed hybrid models named GEP-FARIMA and FFBPNN-FARIMA were found to achieve better estimates of daily ST data at different depths in comparison with the classical models. The daily ST estimates with the highest accuracy were observed at a depth of 50 cm via the GEP-FARIMA at Isfahan station (RMSE = 0.05 °C MAE = 0.03 °C RRMSE = 0.25% for the testing phase) the GEP-FARIMA at Urmia station (RMSE = 0.04 °C MAE = 0.03 °C RRMSE = 0.26% for the testing phase) and the FFBPNN-FARIMA at Rasht station (RMSE = 0.07 °C MAE = 0.05 °C RRMSE = 0.35% for the testing phase). © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 61
Citation - Scopus: 64
Drought modeling using classic time series and hybrid wavelet-gene expression programming models
(ELSEVIER, 2020) Saeid Mehdizadeh; Farshad Ahmadi; Ali Danandeh Mehr; Mir Jafar Sadegh Safari; Mehr, Ali Danandeh; Safari, Mir Jafar Sadegh; Ahmadi, Farshad; Danandeh Mehr, Ali; Mehdizadeh, Saeid
The standardized precipitation evapotranspiration index (SPEI) at three different time scales (i.e. SPEI-3 SPEI-6 and SPEI-12) from six meteorology stations located in Turkey are modeled in this study. To this end two types of classic time series models namely linear autoregressive (AR) and non-linear bi-linear (BL) are used. Furthermore the hybrid models are proposed by coupling the wavelet (W) and gene expression programming (GEP). Five various mother wavelets (i.e. Haar db4 Symlet Meyer and Coifflet) for the first time are employed and compared for implementing the hybrid W-GEP approach in drought modeling. The modeling results of SPEI droughts via the time series models illustrated that the non-linear BL performs slightly better than the linear AR. Moreover all the hybrid W-GEP models developed in the study region provide superior performances compared to the standalone GEP. In general db4 in SPEI-3 modeling and Symlet for modeling the SPEI-6 and SPEI-12 of the studied locations are the optimal wavelets to develop the W-GEP. Finally the SPEI series at each target station is modeled through applying the SPEI data of the five neighboring stations. It is found that the SPEI data of neighboring stations are appropriate for modeling the SPEI series of the target station when the SPEI data of the target station is not at hand. For this case the performance of standalone GEP for modeling the SPEI-3 and SPEI-6 of the stations is generally better than the case of utilizing the original SPEI data at each target station.
Citation - WoS: 15
Citation - Scopus: 16
Estimating the short-term and long-term wind speeds: implementing hybrid models through coupling machine learning and linear time series models
(SPRINGER INT PUBL AG, 2020) Saeid Mehdizadeh; Ali Kozekalani Sales; Mir Jafar Sadegh Safari; Sales, Ali Kozekalani; Safari, Mir Jafar Sadegh; Kozekalani Sales, Ali; Mehdizadeh, Saeid
Wind speed data are of particular importance in the design and management of wind power projects. In the current study three types of linear time series models including autoregressive (AR) moving average (MA) and autoregressive moving average (ARMA) were employed to estimate short-term (i.e. daily) and long-term (i.e. monthly) wind speeds. The required data were gathered respectively from the Tabriz and Zahedan stations in the northwest and southeast of Iran. The MA models outperformed the AR and ARMA on the both daily and monthly scales. Daily and monthly wind speed values as a function of lagged wind speed data were then estimated using two machine learning models of random forests (RF) and multivariate adaptive regression splines (MARS). It was found that the RF and MARS provided similar results, however RF performed slightly better than the MARS. Finally the stand-alone time series and machine learning models were coupled to improve the accuracy of the wind speed estimation. Accordingly the hybrid RF-AR RF-MA RF-ARMA MARS-AR MARS-MA and MARS-ARMA models were implemented. It was concluded that the hybrid models outperformed the stand-alone RF and MARS for both short- and long-term wind speed estimations where the RF-AR and MARS-AR hybrid models provided the best performances. The hybrid models tested in the present study could be effective alternatives to the stand-alone machine learning-based RF and MARS models for the estimation of wind speed time series.
Citation - WoS: 104
Citation - Scopus: 119
Hybrid models to improve the monthly river flow prediction: Integrating artificial intelligence and non-linear time series models
(Elsevier B.V., 2019) Farshad Fathian; Saeid Mehdizadeh; Ali Kozekalani Sales; Mir Jafar Sadegh Safari; Sales, Ali Kozekalani; Fathian, Farshad; Safari, Mir Jafar Sadegh; Kozekalani Sales, Ali; Mehdizadeh, Saeid
Prediction of river flow as a fundamental source of hydrological information plays a crucial role in various fields of water projects. In this study at first the capabilities of two time series analysis approaches namely self-exciting threshold autoregressive (SETAR) and generalized autoregressive conditional heteroscedasticity (GARCH) models then three artificial intelligence approaches including artificial neural networks (ANN) multivariate adaptive regression splines (MARS) and random forests (RF) models were investigated to predict monthly river flow. For this purpose monthly river flow data of Brantford and Galt stations on Grand River Canada for the period from October 1948 to September 2017 were used and their performances were evaluated based on various evaluation criteria. The SETAR model showed better performance than the GARCH one in prediction of river flows at the stations of study. Additionally the stand-alone MARS and RF models performed slightly better than the ANN. Next hybrid models were developed by coupling the used ANN MARS and RF models with SETAR and GARCH models as the non-linear time series models. The performance of various models presented in this study indicated that the new hybrid models demonstrated a much better performance compared with the stand-alone ones at both stations. Among the developed hybrid models the RF-SETAR models generally had the best accuracy to improve the river flows modeling. As a result it can be concluded that the presented methodology can be used to predict hydrological time series such as river flow with a high level of accuracy. © 2019 Elsevier B.V. All rights reserved.